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Added link to new QAT fine-tuning notebook (YoloNAS) + removed github notebooks #1100

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May 30, 2023
Merged

Added link to new QAT fine-tuning notebook (YoloNAS) + removed github notebooks #1100

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aa83893
removed notebooks
shaydeci May 30, 2023
64d3cf4
Merge branch 'master' into feature/SG-876_notebook_links_qat_ptq_yolonas
shaydeci May 30, 2023
4368a9e
fixed links
shaydeci May 30, 2023
a75996a
Merge remote-tracking branch 'origin/feature/SG-876_notebook_links_qa…
shaydeci May 30, 2023
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41 changes: 13 additions & 28 deletions README.md
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Expand Up @@ -120,8 +120,12 @@ ________________________________________________________________________________
pip install super-gradients
```

## What's New - Version 3.1.1 (May 3rd)
## What's New
__________________________________________________________________________________________________________
30th of May
* [Quantization Aware Training YoloNAS on Custom Dataset](https://bit.ly/3MIKdTy)

Version 3.1.1 (May 3rd)
* [YOLO-NAS](https://bit.ly/41WeNPZ)
* New [predict function](https://bit.ly/3oZfaea) (predict on any image, video, url, path, stream)
* [RoboFlow100](https://bit.ly/40YOJ5z) datasets integration
Expand Down Expand Up @@ -204,9 +208,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3qKx9m8"><img src="./docs/assets/SG_img/colab_logo.png" /> Segmentation Quick Start</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3qJjxYq"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source </a>
</td>
</table>
</br></br>

Expand All @@ -217,9 +218,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3qKwMbe"><img src="./docs/assets/SG_img/colab_logo.png" /> Segmentation Transfer Learning</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3ShJlXn"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -230,9 +228,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3QQBVJp"><img src="./docs/assets/SG_img/colab_logo.png" /> Segmentation How to Connect Custom Dataset</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3Us2WGi"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -246,9 +241,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3SkMohx"><img src="./docs/assets/SG_img/colab_logo.png" /> Detection Transfer Learning</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3DF8siG"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -257,9 +249,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3dqDlg3"><img src="./docs/assets/SG_img/colab_logo.png" /> Detection How to Connect Custom Dataset</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3xBlcmq"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -272,9 +261,6 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
<td width="500">
<a target="_blank" href="https://bit.ly/3f4mssd"><img src="./docs/assets/SG_img/colab_logo.png" /> How to Predict Using Pre-trained Model</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3Sf59Tr"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -287,9 +273,14 @@ Quantization involves representing weights and biases in lower precision, result
<td width=“500”>
<a target="_blank" href="http://bit.ly/3KrN6an"><img src="./docs/assets/SG_img/colab_logo.png" /> Post Training Quantization and Quantization Aware Training</a>
</td>
<td width=“200”>
<a target="_blank" href="http://bit.ly/3nUGzxb"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>

### Quantization Aware Training YoloNAS on Custom Dataset
This tutorial provides a comprehensive guide on how to fine-tune a YoloNAS model using a custom dataset. It also demonstrates how to utilize SG's QAT (Quantization-Aware Training) support. Additionally, it offers step-by-step instructions on deploying the model and performing benchmarking.
<table class=“tfo-notebook-buttons” align=“left”>
<td width=“500”>
<a target="_blank" href="https://bit.ly/3MIKdTy"><img src="./docs/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset</a>
</td>
</table>

### Knowledge Distillation Training
Expand All @@ -299,9 +290,6 @@ Learn more about SuperGradients knowledge distillation training with our pre-tra
<td width="500">
<a target="_blank" href="https://bit.ly/3BLA5oR"><img src="./docs/assets/SG_img/colab_logo.png" /> Knowledge Distillation Training</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3S9UlG4"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
</td>
</table>
</br></br>

Expand All @@ -315,9 +303,6 @@ Recipes support out of the box every model, metric or loss that is implemented i
<td width="500">
<a target="_blank" href="https://bit.ly/3UiY5ab"><img src="./docs/assets/SG_img/colab_logo.png" /> How to Use Recipes</a>
</td>
<td width="200">
<a target="_blank" href="https://bit.ly/3QSrHbm"><img src="./docs/assets/SG_img/GitHub_logo.png" /> GitHub source</a>
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</table>
</br></br>

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6 changes: 6 additions & 0 deletions YOLONAS.md
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Expand Up @@ -63,6 +63,12 @@ We demonstrate great performance of YOLO-NAS on downstream tasks. When fine-tuni
<img src="./docs/assets/SG_img/colab_logo.png" /> Fine-Tuning Notebook
</a>
</td>
</tr><tr>
<td>
<a target="_blank" href="https://bit.ly/3MIKdTy">
<img src="./docs/assets/SG_img/colab_logo.png" /> Quantization Aware Training YoloNAS on Custom Dataset Notebook
</a>
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</tr>
<tr>
<td>
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890 changes: 0 additions & 890 deletions tutorials/PTQ_and_QAT_for_classification.ipynb
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